Team Name: The Optimizers
Team Members:
- Ravinder Singh
- Raj Verma
- Ritesh Yadav
- Ayush Prajapati
Submission Date: 13 October 2025
Our team’s solution integrates structured product attributes with a tuned gradient boosting framework to predict optimal product prices. By leveraging log-transformations, feature engineering, and Bayesian optimization through Optuna, we achieved strong predictive performance and model stability across validation folds.
We interpreted the challenge as a regression task, where the objective is to predict a product’s price based on multiple categorical and numerical attributes. Extensive EDA was performed to detect trends, correlations, and feature interactions influencing price.
Key Observations:
- Price distribution is right-skewed; log transformation normalizes the target.
- Product brand and category are the most influential features.
- Missing data treatment and encoding methods (Label Encoding + Target Encoding) significantly improved model accuracy.
- Outlier removal and normalization reduced variance and improved stability.
We implemented a single strong model pipeline using LightGBM and tuned its hyperparameters via Optuna (Bayesian optimization). The log-transformed target made the model more robust to skewed price distributions.
Approach Type: Single Model (LightGBM Regressor)
Core Innovation: Log-transformed target regression with automated Optuna-based hyperparameter optimization and advanced categorical feature encoding.
The following diagram illustrates the complete data flow of the product price prediction pipeline — from raw data ingestion to final model output.
flowchart LR
A[Raw Product Data]
A --> B[Data Preprocessing]
B --> B1[Missing Value Imputation]
B --> B2[Label & Target Encoding]
B --> B3[Log-Transform Target Variable]
A --> C[Feature Engineering]
C --> C1[Derived Product-Level Ratios]
C --> C2[Quantity-Based Normalization]
C --> C3[High-Cardinality Feature Handling]
A --> D[Model Training]
D --> D1[LightGBM Regressor]
D --> D2[Optuna Bayesian Tuning]
D --> E[Validation & Early Stopping]
E --> F[Prediction]
F --> F1[Inverse Log-Transform → Final Price]
Text Processing Pipeline:
- Preprocessing steps: lowercasing, cleaning special characters, label encoding categorical text features
- Model type: LightGBM Regressor
- Key parameters (optimized using Optuna):
- num_leaves
- max_depth
- learning_rate
- feature_fraction
- bagging_fraction
- min_data_in_leaf
- lambda_l1, lambda_l2
Image Processing Pipeline:
- Preprocessing steps: N/A
- Model type: N/A
- Key parameters: N/A
- SMAPE Score: 52.89
- Other Metrics:
- MAE: 152.8
- RMSE: 230.3
- R²: 0.89
Our solution effectively models complex non-linear dependencies between product features and price using an optimized LightGBM pipeline. The log-transformation and Optuna-based tuning contributed significantly to the model’s accuracy and generalization. Future enhancements could include multimodal learning (e.g., image-based product representation) and price trend tracking for dynamic updates.
Code Directory Structure:
flowchart LR
A[📁 Smart_Product_Pricing/]
A --> B[📁 data/]
B --> B1[📄 train.csv]
B --> B2[📄 test.csv]
A --> C[📁 notebooks/]
C --> C1[📓 EDA.ipynb]
A --> D[📁 src/]
D --> D1[⚙️ preprocess.py]
D --> D2[⚙️ feature_engineering.py]
D --> D3[⚙️ train.py]
D --> D4[🎯 optuna_tuning.py]
D --> D5[🔍 inference.py]
A --> E[📁 models/]
E --> E1[🧠 lgbm_best_model.pkl]
A --> F[📘 README.md]
- Feature Importance: Brand, Category, and Quantity emerged as dominant features.
- Training Time: ~2.8 minutes per Optuna trial on GPU (Colab environment).
- Validation Strategy: 5-Fold Stratified Split for better generalization.
- Deployment Readiness: Model serialized with pickle for direct API integration.